Recommendation systems are tools that help people find items on the internet by filtering information so as to show only the most suitable items that match a person's needs or preferences. The recommendation problem is the problem of finding the items that most accurately match the user's preferences. A particular recommendation technique is useful if it can both accurately predict a user's preferences and also make recommendations that are novel (i.e., a recommendation of an item that is already very popular gives nothing new or valuable to a user). Two widely used recommendation techniques are collaborative filtering (CF) and preference-based approach (PBA).
Collaborative filtering (CF) recommends products to users based on the experience of like-minded groups of users under the assumption that similar users like similar objects. Therefore, the ability to recommend items depends on the capability of successfully identifying the set of similar users. CF does not build an explicit model of a user's preferences, but rather preferences remain implicit in the ratings that a user gives to some subset of products. Although CF is currently the most popular recommendation technique, it suffers from a number of drawbacks, including the cold start problem, where a minimum number of ratings must be known in order to find the right neighborhood of similar users, the first rater problem, and scalability problems.
PBA is another widely used technique and, unlike CF, requires the user to input explicit preferences for particular attributes of a product. Preferred products can then be predicted even if the set of alternatives is extremely large and/or volatile. This technique does not suffer from cold start, latency, or scalability problems since recommendations are based only on the individual user's data. The main drawback of PBA, however, is that the user needs to express and input potentially a highly complex preference model, requiring a large number of interactions and placing a higher cognitive load on the user.
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